Reference no: EM131050373 , Length: 70

Introduction

You are a computational neuroscientist working on simulations of neural networks. You have data representing a very large feed-forward network and wish to create a computer simulation for further study. The network can be modelled as a graph with vertices representing neurons and directed edges representing connections between them. In order to calculate the output of a neuron, you need to first calculate the output of all neurons that connect to it (note: this particular feed-forward network has no cycles). So a topological sort will give a possible evaluation order for the network.

Assignment Specification - Part A

For this part of the assignment, you will be given a network and a starting state. Your program will then calculate the subsequent state of the network. Forneurons that have no inputs, the new state will be the same as the current state. For other neurons, a very simple neuron model will be used:

for each neuron v: input = 0

for each edge uv:
 input += weight(uv)*state(u)

if input>0: state(v)= 1
else:
state(v)= 0

You must use the following data structures to represent the graph as an adjacency list:

typedef struct edge{ int toVertex; int weight;
} Edge;

typedef struct edgeNode{ Edge edge;
struct edgeNode *next;
} *EdgeList;

typedef struct graph{ int V;
int *state; EdgeList *edges;
} Graph;

For testing purposes you will input the graph data from the keyboard and/orusing input redirection (as for assignment 1). The input format is as follows:

- The first line will contain the number of vertices ( neurons) in the graph
- Then for each vertex:
o The first line contains the initial state of that vertex
o The second line contains the number of edges from that vertex
o The third line contains a list of toVertex, weight pairs separated by spaces (if there are no edges, this line is omitted)

For example, the following text would define a 7 vertex graph:

7
1
2
6,1 3,-2
0
1
4,4
1
3
4,-2 3,1 0,3
1
0
1
1
3,2
0
4
6,2 3,-4 2,1 0,-3
1
3
5,2 4,-1 3,4

The edge weights will be in the range -100 to +100; states will be either 0 or 1.

Your program should use the queue based topological sort method based on in-degrees to determine an evaluation order for the graph. Then working through the vertices in this topologically sorted order to calculate the next state of each neuron. The output should be the new state of all neurons after this calculation (on a single line). So the initial state of the vertex above would be printed as:

1011101

Assignment Specification - Part B

You find that the topological sort can take a long time for some graphs, and you would also like to be able to detect cycles. So you decide to test whether a depth first search based topological sort gives better performance.

For this part of the assignment, you will implement DFS for the input graph, and use the post- order numbering of the DFS spanning tree to determine if there are any cycles. [Note: you don't have to actually build a separate DFS spanning tree to calculated the post-order numbering] If there are no cycles, you should then use the post-order numbering to calculate a topological sort and calculate and print the new state of the graph as for part A. Otherwise, the program should print an error message: "this graph contains at least one cycle".

There is however, as light complication. The procedure for DFS described in lectures assumes a single known starting vertex. That is not the case for this data, where in general we will have many "input" neurons. In this situation, we usually build a DFS forest by repeated application of the algorithm.

For this assignment, two solutions to this problem will be allowed:

1. Implement an algorithm that builds a DFS spanning forest. You will find examples of this approach online.

2. Use the previously calculated in-degrees to identify "input" neurons (the ones with in-degree 0). Then (conceptually) add a new vertex to the graph with edges to each of these inputs. Make this new vertex the starting point for your DFS. [Note: you don't have to actually add the vertex to the graph, just proceed as though it does exist]

The first approach has the advantage that it is the standard way to deal with this problem, so you will find more help online. The second approach has the advantage that it matches what was discuss in lectures more closely and may be a little simpler to understand.

Attachment:- Data.rar